Business Data Analyst (Llz Financials)

As a Business Data Analyst at LLZ Financials, your primary responsibility will be to collect, analyze, and interpret complex data sets to provide valuable insights and recommendations that drive informed business decisions. You will work closely with stakeholders from various departments to identify data requirements, develop data models, and create reports and visualizations to effectively communicate findings. Your role will involve a combination of technical expertise and business acumen to bridge the gap between data and actionable insights.

Responsibilities:

1. Data Collection and Analysis: Gather data from various sources, clean and transform it as necessary, and perform in-depth analysis to identify trends, patterns, and correlations.
2. Data Modeling and Visualization: Develop data models and create visualizations, dashboards, and reports to present data in a clear and meaningful way to stakeholders.
3. Business Insights: Interpret data and provide insights to support strategic decision-making, optimize business processes, and improve operational efficiency.
4. Data Quality Assurance: Ensure data accuracy, completeness, and integrity by implementing data validation techniques and conducting regular audits.
5. Performance Tracking: Monitor key performance indicators (KPIs) and metrics to track business performance and identify areas for improvement.
6. Collaborative Approach: Collaborate with cross-functional teams to understand business requirements, gather feedback, and align data analysis efforts with organizational goals.
7. Data-driven Recommendations: Generate actionable recommendations based on data analysis to drive business growth, optimize resources, and improve customer experience.

Requirements:

1. Educational Background: Bachelor's degree in a quantitative field such as Mathematics, Statistics, Economics, or Computer Science. A master's degree is preferred but not required.
2. Analytical Skills: Strong analytical and problem-solving skills to analyze complex datasets, identify trends, and extract valuable insights.
3. Technical Proficiency: Proficiency in SQL for data extraction and manipulation. Experience with programming languages such as Python or R is highly desirable. Familiarity with data visualization tools like Tableau or Power BI is a plus.
4. Business Acumen: Understanding of business processes, industry dynamics, and key performance indicators to effectively translate data analysis into actionable business recommendations.
5. Communication Skills: Excellent verbal and written communication skills to present complex data analysis findings to both technical and non-technical stakeholders.
6. Attention to Detail: Strong attention to detail to ensure data accuracy and reliability.
7. Team Player: Ability to work collaboratively in a team environment and effectively engage with stakeholders at all levels of the organization.